This invention relates generally to ambulance deployment and, more particularly, to a network-based system and method for dynamic deployment of ambulances and emergency services using consumer-related data.
For Emergency Medical Services (EMS), including public or private ambulances staffed with Emergency Medical Technicians (EMTs), timing is everything. A few minutes can mean the difference between life and death. Accordingly, private EMS are subject to stringent contracts that stipulate a maximum amount of time for ambulance arrival in response to an emergency call (a “maximum response time”). Failure to meet the contracted response time often results in a per-minute fine. For example, if the maximum response time is 10 minutes, and the ambulance arrives in 13 minutes, the ambulance operator may be subjected to a $10/minute fine for any penalty minutes. (In some cases, there are allowable exclusions, such as the ambulance being stopped by a train or an accident holding up traffic, so in the above example, there may be three “late minutes” but one or two minutes of allowable exclusions, such that the ambulance operator may not be charged the penalty fine for all three late minutes.)
Due to the high cost of ambulance operation, which can reach a half a million dollars per staffed ambulance per year, EMS companies want to serve their respective communities with as few ambulances as are required while still meeting patient-care standards (e.g., the contract response time described above). A common strategy is to deploy ambulances in specific positions about a coverage area, from which they can reach the greatest number of people within the contract response time. Accordingly, ambulances are commonly placed in areas with the highest population density, relative to other areas of the coverage area. Ambulance dispatchers and/or operators (used interchangeably herein) are responsible for determining the optimal ambulance placements to meet this strategy. Typically, they rely on historical dispatch data including past emergency calls, semi-empirical data (e.g., a city or urban area is more populated during employment hours, and suburbs are more populated during non-employment hours) as well as available data identifying potential high-density events that may cause an anomalous congregation of people (e.g., newspapers or local websites that describe upcoming fairs, concerts, etc.). Reviewing this available data to identify these events, however, requires time and effort to analyze and schedule around. In addition, ambulance dispatchers may be unaware of certain high-density events, as the event may be relatively short notice and/or may not be well publicized. It would be beneficial for ambulance dispatchers and to for communities they service to have a system configured to supplement existing ambulance deployment schemes to reduce ambulance response times.